Sourabh Khot¹, Venkata Duvvuri¹, Heejae Roh¹ and Anish Mangipudi², ¹Northeastern University, USA, ²USA

Email will remain a vital marketing tool in 2024. Email marketing involves sending commercial emails to a targeted audience. It currently produces a significant ROI (return on investment) in the marketing sector [1]. This research paper presents a comprehensive study on predicting email open rates, focusing specifically on the influence of subject lines. The open-rate prediction algorithm SLk relies on the semantic features of subject lines utilizing a seed dataset of 4500 anonymized subject lines from diverse business sectors. The algorithm integrates data preprocessing, tokenization, and a custom-built repository of power words and negative words to enhance prediction accuracy. In our experiments the actual open rate margin of error was tracking close to what's allowed as per input error giving confidence that SLk can be directionally used for optimizing subject lines performance without prior history. The findings suggest that precise manipulation of subject line features can significantly improve the efficacy of email campaigns.

Email Marketing, Open Rate Prediction, Subject Line Analysis, Machine Learning, Natural Language Processing